NYPD Shooting Incident Data

Setup

Note, before using knitr please install all missing packages from the code cell below into your environment. Also, I recommended knitting into HTML as it has been optimized for viewing in that format.

Data Source Information

This data was procured from https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic and will be updated annually at the source and any time this .rmd file is knit.

From the source: “List of every shooting incident that occurred in NYC going back to 2006 through the end of the previous calendar year.

This is a breakdown of every shooting incident that occurred in NYC going back to 2006 through the end of the previous calendar year. This data is manually extracted every quarter and reviewed by the Office of Management Analysis and Planning before being posted on the NYPD website. Each record represents a shooting incident in NYC and includes information about the event, the location and time of occurrence. In addition, information related to suspect and victim demographics is also included. This data can be used by the public to explore the nature of shooting/criminal activity. Please refer to the attached data footnotes for additional information about this dataset.”

For more information on the details of this dataset it is recommended to follow the link and access the footnotes pdf found on the landing page website.

Environment Setup

We will first begin by loading in the R packages we intend to use.

Then, we will import the data using a URL directly from the source, this ensures we will capture updates to the data as they come in, whenever this is run again.

# Output all commands run and set a standard plot size
knitr::opts_chunk$set(echo = TRUE, fig.width = 10, fig.height = 6)

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.1     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(lubridate)
library(ggmap)

## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
## ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.

library(ggplot2)
library(gridExtra)

## 
## Attaching package: 'gridExtra'
## 
## The following object is masked from 'package:dplyr':
## 
##     combine

import_url <- read.csv("https://data.cityofnewyork.us/api/views/833y-fsy8/rows.csv?accessType=DOWNLOAD")

Transformation and Exploratory Data Analysis (EDA)

Let’s take a look at the dimensions of this imported data.frame, as well as the variable types of each column, and output a summary.

dim(import_url)

## [1] 25596    19

str(import_url)

## 'data.frame':    25596 obs. of  19 variables:
##  $ INCIDENT_KEY           : int  236168668 231008085 230717903 237712309 224465521 228252164 226950018 237710987 224701998 225295736 ...
##  $ OCCUR_DATE             : chr  "11/11/2021" "07/16/2021" "07/11/2021" "12/11/2021" ...
##  $ OCCUR_TIME             : chr  "15:04:00" "22:05:00" "01:09:00" "13:42:00" ...
##  $ BORO                   : chr  "BROOKLYN" "BROOKLYN" "BROOKLYN" "BROOKLYN" ...
##  $ PRECINCT               : int  79 72 79 81 113 113 42 52 34 75 ...
##  $ JURISDICTION_CODE      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ LOCATION_DESC          : chr  "" "" "" "" ...
##  $ STATISTICAL_MURDER_FLAG: chr  "false" "false" "false" "false" ...
##  $ PERP_AGE_GROUP         : chr  "" "45-64" "<18" "" ...
##  $ PERP_SEX               : chr  "" "M" "M" "" ...
##  $ PERP_RACE              : chr  "" "ASIAN / PACIFIC ISLANDER" "BLACK" "" ...
##  $ VIC_AGE_GROUP          : chr  "18-24" "25-44" "25-44" "25-44" ...
##  $ VIC_SEX                : chr  "M" "M" "M" "M" ...
##  $ VIC_RACE               : chr  "BLACK" "ASIAN / PACIFIC ISLANDER" "BLACK" "BLACK" ...
##  $ X_COORD_CD             : num  996313 981845 996546 1001139 1050710 ...
##  $ Y_COORD_CD             : num  187499 171118 187436 192775 184826 ...
##  $ Latitude               : num  40.7 40.6 40.7 40.7 40.7 ...
##  $ Longitude              : num  -74 -74 -74 -73.9 -73.8 ...
##  $ Lon_Lat                : chr  "POINT (-73.95650899099996 40.68131820000008)" "POINT (-74.00866668999998 40.63636384100005)" "POINT (-73.95566903799994 40.68114495900005)" "POINT (-73.939095905 40.69579171600003)" ...

summary(import_url)

##   INCIDENT_KEY        OCCUR_DATE         OCCUR_TIME            BORO          
##  Min.   :  9953245   Length:25596       Length:25596       Length:25596      
##  1st Qu.: 61593633   Class :character   Class :character   Class :character  
##  Median : 86437258   Mode  :character   Mode  :character   Mode  :character  
##  Mean   :112382648                                                           
##  3rd Qu.:166660833                                                           
##  Max.   :238490103                                                           
##                                                                              
##     PRECINCT      JURISDICTION_CODE LOCATION_DESC      STATISTICAL_MURDER_FLAG
##  Min.   :  1.00   Min.   :0.0000    Length:25596       Length:25596           
##  1st Qu.: 44.00   1st Qu.:0.0000    Class :character   Class :character       
##  Median : 69.00   Median :0.0000    Mode  :character   Mode  :character       
##  Mean   : 65.87   Mean   :0.3316                                              
##  3rd Qu.: 81.00   3rd Qu.:0.0000                                              
##  Max.   :123.00   Max.   :2.0000                                              
##                   NA's   :2                                                   
##  PERP_AGE_GROUP       PERP_SEX          PERP_RACE         VIC_AGE_GROUP     
##  Length:25596       Length:25596       Length:25596       Length:25596      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##    VIC_SEX            VIC_RACE           X_COORD_CD        Y_COORD_CD    
##  Length:25596       Length:25596       Min.   : 914928   Min.   :125757  
##  Class :character   Class :character   1st Qu.:1000011   1st Qu.:182782  
##  Mode  :character   Mode  :character   Median :1007715   Median :194038  
##                                        Mean   :1009455   Mean   :207894  
##                                        3rd Qu.:1016838   3rd Qu.:239429  
##                                        Max.   :1066815   Max.   :271128  
##                                                                          
##     Latitude       Longitude        Lon_Lat         
##  Min.   :40.51   Min.   :-74.25   Length:25596      
##  1st Qu.:40.67   1st Qu.:-73.94   Class :character  
##  Median :40.70   Median :-73.92   Mode  :character  
##  Mean   :40.74   Mean   :-73.91                     
##  3rd Qu.:40.82   3rd Qu.:-73.88                     
##  Max.   :40.91   Max.   :-73.70                     
## 

Feature Removal and Renaming

Looks like we have 19 columns (features) and 25596 rows (data points).
First, let’s remove any features that we won’t be needing for our analysis.

1. JURISDICTION_CODE is pretty broad for localizing shooting incidents so we will end up using BORO instead which will give more insight to our analysis.
2. X_COORD_CD, Y_COORD_CD, and Lon_Lat are all redundant as we will use LONGITUDE and LATITUDE in their place.

Also, let’s rename a few of these for more readability.

# Remove features
import_url <- select(import_url, -JURISDICTION_CODE, -X_COORD_CD, -Y_COORD_CD, -Lon_Lat)
# Rename features
import_url <- import_url %>%
        rename(c('DATE' = 'OCCUR_DATE', 'TIME' = 'OCCUR_TIME','BOROUGH' = 'BORO', 
                'LOCATION' = 'LOCATION_DESC', 'MURDER_FLAG' = 'STATISTICAL_MURDER_FLAG', 
                'PERP_AGE' = 'PERP_AGE_GROUP', 'VICTIM_AGE' = 'VIC_AGE_GROUP', 'VICTIM_SEX' = 'VIC_SEX',
                'VICTIM_RACE' = 'VIC_RACE', 'LATITUDE' = 'Latitude', 'LONGITUDE' = 'Longitude'))
head(import_url)

##   INCIDENT_KEY       DATE     TIME  BOROUGH PRECINCT LOCATION MURDER_FLAG
## 1    236168668 11/11/2021 15:04:00 BROOKLYN       79                false
## 2    231008085 07/16/2021 22:05:00 BROOKLYN       72                false
## 3    230717903 07/11/2021 01:09:00 BROOKLYN       79                false
## 4    237712309 12/11/2021 13:42:00 BROOKLYN       81                false
## 5    224465521 02/16/2021 20:00:00   QUEENS      113                false
## 6    228252164 05/15/2021 04:13:00   QUEENS      113                 true
##   PERP_AGE PERP_SEX                PERP_RACE VICTIM_AGE VICTIM_SEX
## 1                                                 18-24          M
## 2    45-64        M ASIAN / PACIFIC ISLANDER      25-44          M
## 3      <18        M                    BLACK      25-44          M
## 4                                                 25-44          M
## 5                                                 25-44          M
## 6                                                 25-44          M
##                VICTIM_RACE LATITUDE LONGITUDE
## 1                    BLACK 40.68132 -73.95651
## 2 ASIAN / PACIFIC ISLANDER 40.63636 -74.00867
## 3                    BLACK 40.68114 -73.95567
## 4                    BLACK 40.69579 -73.93910
## 5                    BLACK 40.67374 -73.76041
## 6                    BLACK 40.70618 -73.75806

Check for Duplicates and Remove

Next, we will check if there are any missing or duplicated data points, focusing only on the INCIDENT_KEY feature for now. This feature will be the most important for identifying any duplicate entries as they should all be unique.

# Check for any NA or Null values
any(is.na(import_url$INCIDENT_KEY)) | any(is.null(import_url$INCIDENT_KEY))

## [1] FALSE

# Check for duplicates
length(unique(import_url$INCIDENT_KEY))

## [1] 20126

length(import_url$INCIDENT_KEY)

## [1] 25596

Subtracting the results here shows that there are 5470 duplicate data points! Let’s take a closer look to make sure these aren’t false positives.

# Query duplicates to see what they look like
head(filter(import_url, duplicated(import_url$INCIDENT_KEY)))

##   INCIDENT_KEY       DATE     TIME   BOROUGH PRECINCT                  LOCATION
## 1    229643172 06/16/2021 23:22:00     BRONX       52                          
## 2    236363733 11/16/2021 22:39:00 MANHATTAN       14                          
## 3    226542152 04/05/2021 22:10:00     BRONX       44 MULTI DWELL - PUBLIC HOUS
## 4    227647476 05/02/2021 18:18:00 MANHATTAN       23 MULTI DWELL - PUBLIC HOUS
## 5    232496781 08/19/2021 20:32:00  BROOKLYN       77                          
## 6    232390408 08/17/2021 22:20:00  BROOKLYN       73            GROCERY/BODEGA
##   MURDER_FLAG PERP_AGE PERP_SEX      PERP_RACE VICTIM_AGE VICTIM_SEX
## 1       false    18-24        M WHITE HISPANIC      25-44          F
## 2       false      <18        M          BLACK      25-44          M
## 3       false                                       45-64          M
## 4       false    18-24        M WHITE HISPANIC      25-44          M
## 5       false    45-64        M          BLACK      45-64          F
## 6       false                                       18-24          M
##   VICTIM_RACE LATITUDE LONGITUDE
## 1       BLACK 40.86414 -73.89131
## 2       BLACK 40.75165 -73.98434
## 3       BLACK 40.83750 -73.92785
## 4       BLACK 40.78694 -73.94357
## 5       BLACK 40.67036 -73.92680
## 6       BLACK 40.66835 -73.90652

# Check a few entries
arrange(filter(import_url, INCIDENT_KEY == 227647476 | INCIDENT_KEY == 232390408), INCIDENT_KEY)

##   INCIDENT_KEY       DATE     TIME   BOROUGH PRECINCT                  LOCATION
## 1    227647476 05/02/2021 18:18:00 MANHATTAN       23 MULTI DWELL - PUBLIC HOUS
## 2    227647476 05/02/2021 18:18:00 MANHATTAN       23 MULTI DWELL - PUBLIC HOUS
## 3    227647476 05/02/2021 18:18:00 MANHATTAN       23 MULTI DWELL - PUBLIC HOUS
## 4    232390408 08/17/2021 22:20:00  BROOKLYN       73            GROCERY/BODEGA
## 5    232390408 08/17/2021 22:20:00  BROOKLYN       73            GROCERY/BODEGA
##   MURDER_FLAG PERP_AGE PERP_SEX      PERP_RACE VICTIM_AGE VICTIM_SEX
## 1       false      <18        M          BLACK      25-44          M
## 2       false    18-24        M WHITE HISPANIC      25-44          M
## 3       false    18-24        M          BLACK      25-44          M
## 4       false                                       25-44          M
## 5       false                                       18-24          M
##   VICTIM_RACE LATITUDE LONGITUDE
## 1       BLACK 40.78694 -73.94357
## 2       BLACK 40.78694 -73.94357
## 3       BLACK 40.78694 -73.94357
## 4       BLACK 40.66835 -73.90652
## 5       BLACK 40.66835 -73.90652

# Yes those are definitely duplicates
# Remove duplicates
import_url <- filter(import_url, !duplicated(import_url$INCIDENT_KEY))
# Check work
length(duplicated(import_url$INCIDENT_KEY))

## [1] 20126

That should do it for the duplicated data points. Let’s continue our transformations.

Change Feature Class Types

For better analysis we should change the class type of a few of these features to make them easier to work with.

# Character to Date and Period
import_url <- import_url %>%
        mutate(DATE = mdy(DATE)) %>%
        mutate(TIME = hms(TIME))

# Character to Factors - changes all character columns to factor
import_url <- import_url %>%
        mutate(across(where(is.character), as.factor))
str(import_url)

## 'data.frame':    20126 obs. of  15 variables:
##  $ INCIDENT_KEY: int  236168668 231008085 230717903 237712309 224465521 228252164 226950018 237710987 224701998 225295736 ...
##  $ DATE        : Date, format: "2021-11-11" "2021-07-16" ...
##  $ TIME        :Formal class 'Period' [package "lubridate"] with 6 slots
##   .. ..@ .Data : num  0 0 0 0 0 0 0 0 0 0 ...
##   .. ..@ year  : num  0 0 0 0 0 0 0 0 0 0 ...
##   .. ..@ month : num  0 0 0 0 0 0 0 0 0 0 ...
##   .. ..@ day   : num  0 0 0 0 0 0 0 0 0 0 ...
##   .. ..@ hour  : num  15 22 1 13 20 4 21 19 0 6 ...
##   .. ..@ minute: num  4 5 9 42 0 13 8 30 18 15 ...
##  $ BOROUGH     : Factor w/ 5 levels "BRONX","BROOKLYN",..: 2 2 2 2 4 4 1 1 3 2 ...
##  $ PRECINCT    : int  79 72 79 81 113 113 42 52 34 75 ...
##  $ LOCATION    : Factor w/ 40 levels "","ATM","BANK",..: 1 1 1 1 1 1 10 1 1 1 ...
##  $ MURDER_FLAG : Factor w/ 2 levels "false","true": 1 1 1 1 1 2 2 1 1 2 ...
##  $ PERP_AGE    : Factor w/ 10 levels "","<18","1020",..: 1 7 2 1 1 1 1 1 1 6 ...
##  $ PERP_SEX    : Factor w/ 4 levels "","F","M","U": 1 3 3 1 1 1 1 1 1 3 ...
##  $ PERP_RACE   : Factor w/ 8 levels "","AMERICAN INDIAN/ALASKAN NATIVE",..: 1 3 4 1 1 1 1 1 1 5 ...
##  $ VICTIM_AGE  : Factor w/ 6 levels "<18","18-24",..: 2 3 3 3 3 3 2 3 3 3 ...
##  $ VICTIM_SEX  : Factor w/ 3 levels "F","M","U": 2 2 2 2 2 2 2 2 2 2 ...
##  $ VICTIM_RACE : Factor w/ 7 levels "AMERICAN INDIAN/ALASKAN NATIVE",..: 3 2 3 3 3 3 3 3 4 7 ...
##  $ LATITUDE    : num  40.7 40.6 40.7 40.7 40.7 ...
##  $ LONGITUDE   : num  -74 -74 -74 -73.9 -73.8 ...

More Feature Checks

We will continue to look at the features and see if any of these blank entries will cause trouble during the analysis. Also, we’ll look to see if there are any duplicate categorical factors in the rest of the features.

# Create a table of each column to check factor levels
for (i in 1:length(import_url)){
    ifelse(is.factor(import_url[ ,i]), print(table(import_url[ ,i, drop = FALSE])), next)
}

## BOROUGH
##         BRONX      BROOKLYN     MANHATTAN        QUEENS STATEN ISLAND 
##          5611          8357          2536          3031           591 
## LOCATION
##                                                 ATM                      BANK 
##                     11964                         1                         2 
##            BAR/NIGHT CLUB         BEAUTY/NAIL SALON               CANDY STORE 
##                       400                        77                         6 
##               CHAIN STORE                CHECK CASH         CLOTHING BOUTIQUE 
##                         5                         1                        11 
##           COMMERCIAL BLDG                DEPT STORE            DOCTOR/DENTIST 
##                       194                         5                         1 
##                DRUG STORE       DRY CLEANER/LAUNDRY         FACTORY/WAREHOUSE 
##                         7                        24                         5 
##                 FAST FOOD               GAS STATION            GROCERY/BODEGA 
##                        77                        50                       468 
##      GYM/FITNESS FACILITY                  HOSPITAL               HOTEL/MOTEL 
##                         3                        37                        25 
##             JEWELRY STORE              LIQUOR STORE              LOAN COMPANY 
##                         9                        24                         1 
##   MULTI DWELL - APT BUILD MULTI DWELL - PUBLIC HOUS                      NONE 
##                      2007                      3664                       140 
##          PHOTO/COPY STORE                 PVT HOUSE          RESTAURANT/DINER 
##                         1                       641                       153 
##                    SCHOOL                SHOE STORE            SMALL MERCHANT 
##                         1                         4                        18 
## SOCIAL CLUB/POLICY LOCATI          STORAGE FACILITY        STORE UNCLASSIFIED 
##                        43                         1                        28 
##               SUPERMARKET           TELECOMM. STORE             VARIETY STORE 
##                        14                         2                        10 
##               VIDEO STORE 
##                         2 
## MURDER_FLAG
## false  true 
## 16604  3522 
## PERP_AGE
##             <18    1020   18-24     224   25-44   45-64     65+     940 UNKNOWN 
##    8120     993       1    4086       1    3769     374      44       1    2737 
## PERP_SEX
##           F     M     U 
##  8090   212 10469  1355 
## PERP_RACE
##                                AMERICAN INDIAN/ALASKAN NATIVE 
##                           8090                              2 
##       ASIAN / PACIFIC ISLANDER                          BLACK 
##                             88                           7871 
##                 BLACK HISPANIC                        UNKNOWN 
##                            803                           1627 
##                          WHITE                 WHITE HISPANIC 
##                            213                           1432 
## VICTIM_AGE
##     <18   18-24   25-44   45-64     65+ UNKNOWN 
##    1984    7585    9122    1273     125      37 
## VICTIM_SEX
##     F     M     U 
##  1522 18598     6 
## VICTIM_RACE
## AMERICAN INDIAN/ALASKAN NATIVE       ASIAN / PACIFIC ISLANDER 
##                              8                            260 
##                          BLACK                 BLACK HISPANIC 
##                          14742                           1854 
##                        UNKNOWN                          WHITE 
##                             50                            517 
##                 WHITE HISPANIC 
##                           2695

Looks like there are quite a few blank entries and a few labeled as “UNKNOWN”.

• Over 50% of the LOCATION and PERP_AGE data is unknown.
• BOROUGH, MURDER_FLAG, PRECINCT, DATE, TIME, LONGITUDE, and LATITUDE have no missing entries.
• The remaining features are missing a few, but are not a significant amount compared to the overall size of the dataset.

We will combine these by labeling all blanks as “UNKNOWN”. This data likely comes from officers on the scene who may have: 1. missed some information; 2. did not have a witness and/or did not catch the offender; or 3. not have previously recorded this particular data but now are due to process changes. It’s reasonable to leave these missing data points in because the missing data also gives us information about that incident. Also, removing these data points due to their empty entries would be a mistake considering the data that is complete holds more relevance. Removing it would be like throwing away the baby with the bathwater (e.g. removing a data point that is missing the LOCATION description, but isn’t missing the rest of the information will only hurt our analysis. Especially considering BOROUGH,LONGITUDE, andLATITUDE aren’t missing).

Here we will combine and correct any missing entries with the methods discussed above.

for (i in 1:length(import_url)){
    # IF the column is a factor AND contains empty space OR a 'U'
    if(is.factor(import_url[ ,i]) && '' %in% import_url[ ,i] || 'U' %in% import_url[ ,i]){
        # Add level named UNKNOWN
        levels(import_url[, i]) <- c(levels(import_url[, i]), 'UNKNOWN')
        # Replace missing values and Us with UNKNWON
        import_url[, i][import_url[, i] == ''] <- as.factor('UNKNOWN')
        import_url[, i][import_url[, i] == 'U'] <- as.factor('UNKNOWN')
        # Remove unused levels
        import_url[, i] <- droplevels(import_url[, i])
    }else{
        next
    }
}

Lastly there are a few values in PERP_AGE that look like data entry typos. See table above:(‘1020’, ‘224’, ‘940’) Here, we cannot assume what was intended so we will change these age values to ‘UNKNOWN’.

# Before changes
table(import_url$PERP_AGE)

## 
##     <18    1020   18-24     224   25-44   45-64     65+     940 UNKNOWN 
##     993       1    4086       1    3769     374      44       1   10857

# Set values we want to keep as levels
age_levels <- c('<18', '18-24', '25-44', '45-64', '65+', 'UNKNOWN')
# Find all values NOT in age_levels (notice the !)
import_url$PERP_AGE[!import_url$PERP_AGE %in% age_levels] <- as.factor('UNKNOWN')
# Remove unused levels
import_url$PERP_AGE <- droplevels(import_url$PERP_AGE)
# After changes
table(import_url$PERP_AGE)

## 
##     <18   18-24   25-44   45-64     65+ UNKNOWN 
##     993    4086    3769     374      44   10860

EDA Cont.

Let’s take a look at a summary of this data now that we’ve cleaned it up.

summary(import_url)

##   INCIDENT_KEY            DATE                 TIME                          
##  Min.   :  9953245   Min.   :2006-01-01   Min.   :0S                         
##  1st Qu.: 62525686   1st Qu.:2009-06-07   1st Qu.:3H 25M 0S                  
##  Median : 87157256   Median :2012-10-09   Median :15H 3M 30S                 
##  Mean   :113047411   Mean   :2013-07-03   Mean   :12H 39M 18.7757130080936S  
##  3rd Qu.:166867508   3rd Qu.:2017-07-07   3rd Qu.:20H 45M 0S                 
##  Max.   :238490103   Max.   :2021-12-31   Max.   :23H 59M 0S                 
##                                                                              
##           BOROUGH        PRECINCT                           LOCATION    
##  BRONX        :5611   Min.   :  1.00   UNKNOWN                  :11964  
##  BROOKLYN     :8357   1st Qu.: 44.00   MULTI DWELL - PUBLIC HOUS: 3664  
##  MANHATTAN    :2536   Median : 69.00   MULTI DWELL - APT BUILD  : 2007  
##  QUEENS       :3031   Mean   : 66.32   PVT HOUSE                :  641  
##  STATEN ISLAND: 591   3rd Qu.: 81.00   GROCERY/BODEGA           :  468  
##                       Max.   :123.00   BAR/NIGHT CLUB           :  400  
##                                        (Other)                  :  982  
##  MURDER_FLAG      PERP_AGE        PERP_SEX    
##  false:16604   <18    :  993   F      :  212  
##  true : 3522   18-24  : 4086   M      :10469  
##                25-44  : 3769   UNKNOWN: 9445  
##                45-64  :  374                  
##                65+    :   44                  
##                UNKNOWN:10860                  
##                                               
##                           PERP_RACE      VICTIM_AGE     VICTIM_SEX   
##  AMERICAN INDIAN/ALASKAN NATIVE:   2   <18    :1984   F      : 1522  
##  ASIAN / PACIFIC ISLANDER      :  88   18-24  :7585   M      :18598  
##  BLACK                         :7871   25-44  :9122   UNKNOWN:    6  
##  BLACK HISPANIC                : 803   45-64  :1273                  
##  UNKNOWN                       :9717   65+    : 125                  
##  WHITE                         : 213   UNKNOWN:  37                  
##  WHITE HISPANIC                :1432                                 
##                          VICTIM_RACE       LATITUDE       LONGITUDE     
##  AMERICAN INDIAN/ALASKAN NATIVE:    8   Min.   :40.51   Min.   :-74.25  
##  ASIAN / PACIFIC ISLANDER      :  260   1st Qu.:40.67   1st Qu.:-73.94  
##  BLACK                         :14742   Median :40.70   Median :-73.92  
##  BLACK HISPANIC                : 1854   Mean   :40.74   Mean   :-73.91  
##  UNKNOWN                       :   50   3rd Qu.:40.82   3rd Qu.:-73.88  
##  WHITE                         :  517   Max.   :40.91   Max.   :-73.70  
##  WHITE HISPANIC                : 2695

Changing many of these features from characters into factors really improves R’s ability to summarize the data here. We can make many conclusions about this data set just from looking at the summary.

• The earliest data point is from January 1, 2006.
• The most recent is from December, 31 2021.
• The distribution of DATE is slightly right-skewed, with a mean 9 months in the future of the median. This suggests there were more incidents from 2006-2014 than 2014-2021.
• Out of the 20126 shooting incidents, 3522 are associated with a murder, ~17.5%.
• Majority of perpetrators are between the ages of 18-24 and 25-44.
• The vast majority of identified perpetrators are male.
• The victim’s age are a little more equally distributed than the perp’s but the vast majority are also between 18-44.

Let’s take a closer look and check the DATE distribution skewness by finding the midpoint date of the data set (this is the exact middle date of this data set).

data_interval <- interval(min(import_url$DATE), max(import_url$DATE))
int_start(data_interval) + (int_end(data_interval) - int_start(data_interval)) / 2

## [1] "2013-12-31 12:00:00 UTC"

median(import_url$DATE)

## [1] "2012-10-09"

Interesting, this proves there were more incidents in the first half of the time interval of the data set. We will look further into this during visualizations.

Visualizations

Now that the transformations are complete, let’s start plotting these features against each other and visualizing our data so we can try to make some conclusions and more clearly inform them.

Let’s begin by visualizing the number of shooting incidents by the BOROUGH they occurred.

# Order factor levels of BOROUGH based on frequency
import_url$BOROUGH <- fct_infreq(import_url$BOROUGH)
# Plot graph
import_url %>%
   ggplot(., aes(x = reorder(BOROUGH, BOROUGH, length, decreasing = TRUE), fill = BOROUGH)) +
   geom_bar(aes(y = after_stat(count))) +
   scale_fill_brewer(palette = 'YlOrRd', direction = -1) +
   theme(axis.text.x = element_text(angle = 25, hjust = 1)) +
   labs(title = 'Number of Shooting Incidents by Borough',
        x = 'Borough', y = 'Number of Incidents',
        caption = 'Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>')

As you can see the majority of shooting incidents have occurred in Brooklyn followed by the Bronx and Queens.

Now to plot the number of shooting incidents organized by the LOCATION description assigned to them.

import_url %>%
   ggplot(., aes(x = reorder(LOCATION, LOCATION, length, decreasing = TRUE), fill = LOCATION)) + 
   geom_bar(aes(y = after_stat(count)), show.legend = FALSE) +
   theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
   scale_y_log10() +
   labs(title = 'Number of Shooting Incidents by Location Description',
        x = 'Location Description', y = 'Number of Incidents (Log Scaled)',
        caption = 'Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>')

Something to note, this graph has been logarithmically scaled on the y-axis for easier viewing.

• “Multi dwell-public housing” shows the most shooting incidents, followed by “Multi-dwell-apt building”, and “Pvt house”.
• This graph shows evidence that a large portion of shooting incidents in NYC occur near the homes of those involved, and though we can’t say for sure, this suggests altercations are not between complete strangers.
• This also shows just how many of these incidents were left without a location description. This could have been left empty for many reasons – a shooting was reported but it was unclear exactly from where, it occurred on the street, etc. Again, it is still important to look at the remaining data we do have as it is still quite a sizeable sample.

# Group by month and total # of incidents
inc_month_totals <- import_url %>%
    group_by(month_totals = months(DATE)) %>%
    summarize(n = n()) %>%
    arrange(match(month_totals, month.name)) %>%
    mutate(month_totals = factor(month_totals, levels = month.name))

# Plot the tibble
month_plot <- ggplot(inc_month_totals, aes(x = month_totals, y = n, fill = month_totals)) +
    geom_col()+
    scale_fill_brewer(palette = 'Paired', direction = 1, name = 'Month') +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = 'Shooting Incidents per Month',
        x = 'Month', y = 'Number of Incidents',
        caption = 'Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>')

# Group by hour and total # of incidents
inc_hour_totals <- import_url %>%
    group_by(hour_totals = hour(TIME)) %>%
    summarize(n = n()) %>%
    mutate(hour_totals = factor(hour_totals))

# Plot
hour_plot <- ggplot(inc_hour_totals, aes(x = hour_totals, y = n, fill = hour_totals)) +
    geom_col(show.legend = FALSE) +
    labs(title = 'Shooting Incidents per Hour of the Day',
        x = 'Hour (24H)', y = 'Number of Incidents',
        caption = 'Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>')

# Plot side-by-side
grid.arrange(month_plot, hour_plot, nrow = 1)

• Like with other crimes, we see a higher amount during the warmer months.
• Shootings are much more likely during nighttime hours.
• Shootings hit a maximum peak at 11:00 (23:00).
• Incidents happen much less often in the morning when people are starting their day and commuting with a minimum at 7:00-9:59.

Looking at these makes me ask the question, “have shootings becoming more or less frequent over the years”. Let’s find out what this data suggests the answer is next.

inc_year_totals <- import_url %>%
    group_by(year_totals = year(DATE)) %>%
    summarize(n = n()) %>%
    mutate(year_totals = factor(year_totals))

year_plot <- ggplot(inc_year_totals, aes(x = year_totals, y = n, fill = year_totals)) +
    geom_col(show.legend = FALSE) +
    theme(axis.text.x = element_text(angle = 25, hjust = 1)) +
    labs(title = 'Shooting Incidents per Year',
        x = 'Year', y = 'Number of Incidents',
        caption = 'Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>')
year_plot

• The number of shootings consistently trend down until 2020.
• This further shows that there were more shootings during 2006-2014 than 2014-2021.
• Interestingly, the Covid pandemic seems to have affected the number of shooting incidents in the opposite way as would be expected.

Let’s take a closer look at 2020 and 2021 but include the months to see if the pandemic had any noticeable influence. We’ll also include 2018-2021 for context.

covid_slice <- import_url %>%
    filter(DATE > '2018-01-01') %>%
    mutate(MONTH = stamp('January', orders = '%B', quiet = TRUE)(DATE)) %>%
    mutate(YEAR = stamp('2020', orders = 'y', quiet = TRUE)(DATE))

covid_months_totals <- covid_slice %>%
    group_by(MONTH, YEAR) %>%
    summarize(n = sum(n())) %>%
    arrange(match(MONTH, month.name)) %>%
    mutate(MONTH = factor(MONTH, levels = month.name))

## `summarise()` has grouped output by 'MONTH'. You can override using the
## `.groups` argument.

ggplot(covid_months_totals, aes(x = MONTH, y = n, fill = YEAR)) +
    geom_col() +
    scale_fill_brewer(palette = 'Spectral', direction = 1, name = 'Year') +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = 'Stacked Barchart Shooting Incidents per Year 2018-2021',
        x = 'Month', y = 'Number of Incidents',
        caption = 'Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>')

Interesting, here it shows there was an increase in shooting incidents beginning in May and June of 2020, coming off a downward trend from previous years. Let’s take another view here and include the date when New York City initiated their lockdown to see if it aligns with this increase.

covid_slice_2 <- import_url %>%
    filter(DATE > '2018-01-01') %>%
    mutate(DATE = stamp('2020-01', orders = 'ym', quiet = TRUE)(DATE))

covid_months_totals_2 <- covid_slice_2 %>%
    group_by(DATE) %>%
    summarize(n = sum(n()))

ggplot(covid_months_totals_2, aes(x = DATE, y = n, fill = DATE)) +
    geom_col(show.legend = FALSE) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
    geom_vline(xintercept = 27.25) +
    annotate('label', x = 27.25, y = 175, angle = 90, color = 'black', label = 'Covid Pandemic Lockdown Initiated') +
    annotate("rect", xmin = 0, xmax = 27.25, ymin = 0, ymax = 250, alpha = .2, fill = "#00c9d0") +
    annotate("rect", xmin = 27.25, xmax = 48.5, ymin = 0, ymax = 250, alpha = .2, fill = "#c42a07") +
    labs(title = 'Shooting Incidents per Year 2018-2021',
        x = 'Month', y = 'Number of Incidents',
        caption = 'Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>')

There certainly is some underlying correlation here, but from this data we can’t say for sure what that is exactly. That said, this is an interesting result because the increase in shootings seems to correlate to the period of time where the pandemic lockdown in NYC would have been in affect for over a month and the increase in shootings occurs when restrictions would have been begun to loosen.

Incident Coordinate Data Visualized on a Map

For reference here is a map of NYC’s boroughs in the public domain from Wikipedia: https://commons.wikimedia.org/w/index.php?title=Special:Redirect/file/5_Boroughs_Labels_New_York_City_Map.svg

reference_map <- 'https://commons.wikimedia.org/w/index.php?title=Special:Redirect/file/5_Boroughs_Labels_New_York_City_Map.svg'
knitr::include_graphics(reference_map)

Labels on borough reference map:

1. Manhattan
2. Brooklyn
3. Queens
4. The Bronx
5. Staten Island

Here we’re going to visualize the location of each shooting incident using the coordinates given in the dataset. First, we can use the minimum and maximum values of the longitudes and latitudes to find the map’s bounding box (edges). Then, use ggmap() to generate a map centered around these coordinates. Then, we can use geom_point() and stat_density2d_filled() to superimpose our data on the map using the same coordinate system we generated.

# Initialize the bounding box that will contain the map view edges
map_bounds <- c(
    left = min(import_url$LONGITUDE),
    bottom = min(import_url$LATITUDE),
    right = max(import_url$LONGITUDE),
    top = max(import_url$LATITUDE))

# Initialize the map of NYC using map_bounds
# Note, there are better maps out there but most require a private google API key,
# which wouldn't work for this public, knit-able, project.
incident_map_point <- ggmap(get_stamenmap(map_bounds, maptype = 'terrain', zoom = 11)) + 
# Overlay each data point using LONG. and LAT.
    geom_point(data = import_url,
            aes(x = LONGITUDE, y = LATITUDE),
            color = 'darkred',
            size = 0.25,
            alpha = 0.5) +
    ggtitle('Point Plot of NYPD Shooting Incident Reporting 2006 - 2021') +
    labs(x = 'Longitude', y = 'Latitude',
    caption = 'Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>')

## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

incident_map_point_color <- ggmap(get_stamenmap(map_bounds, maptype = 'terrain', zoom = 11)) + 
# Overlay each data point using LONG. and LAT.
    geom_point(data = import_url,
        aes(x = LONGITUDE, y = LATITUDE, color = BOROUGH),
        size = 0.25,
        alpha = 0.5) +
    scale_color_brewer(palette = 'Set1', direction = 1, name = 'Borough') +
    theme(legend.position = 'bottom') +
    guides(color = guide_legend(override.aes = list(size = 5, alpha = 1))) +
    ggtitle('Point Plot of NYPD Shooting Incident Reporting 2006 - 2021') +
    labs(x = 'Longitude', y = 'Latitude',
    caption = 'Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>')

## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

# Display Point Map
grid.arrange(incident_map_point, incident_map_point_color, ncol = 2)

This looks great and gives a much deeper understanding of the spacial distribution of the shootings, much more than the previous borough plot.

• Central Park seems to be quite safe from shootings (though this may be due to the methods of recording incidents).
• The North side of Manhattan is responsible for the majority of the shootings in that borough.
• Comparatively, the distribution of shootings in Queens is quite evenly spread, and mostly on the South side.
• There are some hot spots that would not be obvious without the coordinate data, namely around the coasts.

The point map does have limitations though. There is a loss of information when many points overlap. It’s hard to compare the higher density areas to one another (e.g. we know from the previous graph that Brooklyn has >2000 more incidents than The Bronx, but here you really can’t parse that out). Let’s try to convert this into a heat/density map to get a better picture of these higher density areas.

# Initialize density map to better visualize regions with frequent incidents.
incident_map_density <- ggmap(get_stamenmap(map_bounds, maptype = 'terrain', zoom = 11)) + 
    stat_density2d_filled(data = import_url,
        contour_var = 'density',
        aes(x = LONGITUDE, y = LATITUDE, fill = after_stat(level)),
        bins = 20,
        geom = 'polygon',
        alpha = 0.8) +
    geom_density_2d(data = import_url,
        aes(x = LONGITUDE, y = LATITUDE),
        bins = 20,
        alpha = 0.2,
        color = "white") +
    guides(fill = guide_legend(title = "Density")) +
    ggtitle('Density Plot of NYPD Shooting Incident Reporting 2006 - 2021') +
    labs(x = 'Longitude', y = 'Latitude',
    caption = 'Source:<https://catalog.data.gov/dataset/nypd-shooting-incident-data-historic>')

## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

# Display Density Map
incident_map_density

• Now it’s much more obvious that Brooklyn exhibits more shooting incidents than The Bronx, denoted here by a more intense yellow/green.
• Though the total amount in Manhattan is lower than Queens, you can see it’s much more concentrated to one area in Manhattan.
• The masking of the lower density areas highlights some areas we may have overlooked on the scatter plot.

Conclusion

Even features that initially seemed sparse, considering the number of missing entries, led to valuable insights into this data.

Some takeaways we’ve seen from above:

1. While the overall trend in number of shooting incidents was going down year after year from 2006 to the beginning of 2020, they saw a large increase from right after the Covid pandemic began.
2. Majority of shootings in the dataset occur in Brooklyn, The Bronx, and near public and private housing.
3. Even in low incident areas there are identifiable hotspots that most shootings occur (e.g. Staten Island, though low on the totals scale, shows ~5 localized clusters of incidents).
4. While women make up ~8.18% of the victims in the cases where the sex was known, they only make up ~2.03% of the perpetrators where the sex was known.
5. The north side of Manhattan is responsible for a large portion of the number of incidents in the borough.

Bias

Much of the bias of this analysis comes from choosing what features to include or not and possibly what conclusions can be drawn. My experiences while living in two different metropolitan cities for half my life or my past experiences with police officers could also affect the lens I view this information. Also, ever the optimist I assumed shootings would have seen a reduction during the pandemic, but seeing the data now I can see another story. In an attempt to mitigate these biases I’ve chosen to only include what I believe the data to empirically show, and not obfuscate any methods in obtaining these results.

Another bias that is likely present here is how the data was collected, reviewed, and updated. We don’t know the exact method in this case, and it is likely working off of hundreds of police officers’ efforts over the years who went about their work with their own biases. These are all things to keep in mind when working with these datasets and drawing conclusions from them. Hopefully I’ve made the conclusions made here reproducible and clear.